Find Restaurants by Cuisine and Rating on Google Maps (2026)

Thirdwatch's Google Maps Scraper returns cuisine-and-rating-filtered restaurants on a pay-per-record basis — name, address, rating, reviews_count, phone, website, opening_hours, lat/lng, place_id. Built for restaurant-aggregator builders, food-delivery competitive intelligence, hospitality-research teams, and operators scoping local-market opportunities.

Why filter Google Maps restaurants by cuisine and rating

Restaurant discovery is the most-used Google Maps query category. According to Google's 2024 Local Search statistics, more than 30% of all Google Maps searches involve a food/restaurant intent, and rating + review-count is the canonical filter pattern users apply. For aggregator builders, market-research teams, and operators benchmarking competitors, cuisine-and-rating filtering is the foundation of restaurant-discovery pipelines.

The job-to-be-done is structured. A food-delivery startup wants 10 cuisines × 20 cities = 200 restaurant lists ranked by rating for marketplace seeding. A hospitality-research team wants 4.5+ rated Italian restaurants in Manhattan to study micro-cuisine patterns. A restaurant-tech SaaS wants per-cuisine market-share data per metro. A franchise operator wants competitor-restaurant maps per neighbourhood. All reduce to cuisine + city queries + rating filter on the returned dataset.

How does this compare to the alternatives?

Three options for cuisine-and-rating-filtered restaurant data:

Yelp's official API is restaurant-focused but rate-limited and gated behind approval. Foursquare's Places API is faster to onboard but pricier per-result. The Google Maps Scraper actor page gives you Google's broader index at the lowest unit cost.

How to find restaurants by cuisine and rating in 4 steps

Step 1: How do I authenticate against Apify?

Sign in at apify.com (free tier, no credit card), open Settings → Integrations, and copy your personal API token. Every example below assumes the token is in APIFY_TOKEN:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull a multi-cuisine, multi-city batch?

Pass cuisine + city query strings as an array.

import os, requests, pandas as pd
from itertools import product

ACTOR = "thirdwatch~google-maps-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

CUISINES = ["italian", "japanese", "thai", "mexican", "indian",
            "chinese", "french", "mediterranean", "vietnamese", "korean"]
CITIES = ["New York", "San Francisco", "Chicago", "Los Angeles", "Boston"]

queries = [f"{c} restaurant {city}" for c, city in product(CUISINES, CITIES)]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={"queries": queries, "maxResults": 50},
    timeout=3600,
)
df = pd.DataFrame(resp.json())
print(f"{len(df)} restaurants across {df.address.str.split(',').str[-2].str.strip().nunique()} cities")

10 cuisines × 5 cities = 50 queries × 50 results = up to 2,500 restaurants — well within budget for a one-off directory build.

Step 3: How do I filter by rating and review threshold?

Apply rating and reviews_count thresholds for serious-operator filtering.

df["rating"] = pd.to_numeric(df.rating, errors="coerce")
df["reviews_count"] = pd.to_numeric(df.reviews_count, errors="coerce")

quality = df[
    (df.rating >= 4.3)
    & (df.reviews_count >= 50)
    & df.phone.notna()
].sort_values(["rating", "reviews_count"], ascending=[False, False])

print(f"{len(quality)} quality restaurants")
print(quality[["name", "rating", "reviews_count", "address", "phone"]].head(20))

The (rating >= 4.3) & (reviews_count >= 50) threshold filters the long tail of unrated/under-reviewed listings while preserving 4.3-4.5 mid-range operators. Tightening to rating >= 4.6 & reviews_count >= 200 produces a curated "best of" list per cuisine per metro.

Step 4: How do I export per-cuisine, per-metro top lists?

Group by cuisine + city and emit Markdown for an editorial directory.

import re, pathlib

def cuisine_from_query(q):
    return q.split(" restaurant ")[0]

df["cuisine"] = df.searchString.apply(cuisine_from_query)
df["metro"] = df.address.str.extract(r",\s*([^,]+),\s*[A-Z]{2}")

for (cuisine, metro), grp in quality.groupby(["cuisine", "metro"]):
    if len(grp) < 5:
        continue
    out = pathlib.Path(f"directory/{cuisine}_{metro.replace(' ', '_')}.md")
    out.parent.mkdir(parents=True, exist_ok=True)
    lines = [f"# Top {cuisine.title()} Restaurants in {metro}\n"]
    for _, r in grp.head(10).iterrows():
        lines.append(f"## {r['name']}\n- Rating: {r.rating} ({int(r.reviews_count)} reviews)\n- Address: {r.address}\n- Phone: {r.phone}\n")
    out.write_text("\n".join(lines))

print(f"Exported {len(list(pathlib.Path('directory').glob('*.md')))} cuisine × metro lists")

The Markdown files are ready to ingest into a static-site generator (Astro, Hugo, Next.js) for an editorial restaurant-directory product.

Sample output

A single restaurant record looks like this. Five rows weigh ~7 KB.

{
  "name": "L'Industrie Pizzeria",
  "category": "Pizza restaurant",
  "address": "254 S 2nd St, Brooklyn, NY 11211",
  "phone": "+1 718-599-0002",
  "website": "https://lindustriebk.com",
  "rating": 4.7,
  "reviews_count": 1842,
  "lat": 40.7104,
  "lng": -73.9608,
  "place_id": "ChIJrTLr-GyuEmsRBfy61i59si0",
  "opening_hours": ["Mon: Closed", "Tue: 12-10 PM", "Wed-Sun: 12-11 PM"]
}

place_id is the canonical natural key for cross-snapshot dedup — stable across rating changes and rename events. opening_hours is the structured day-by-day schedule (now extracted reliably from inner[203] of the search response). category provides Google's primary cuisine tag, useful for cuisine-purity filtering before the keyword-based extension. The rating + reviews_count pair provides the strongest signal of restaurant quality and consensus.

Common pitfalls

Three things go wrong in cuisine-and-rating filtering pipelines. Cuisine tag drift — Google Maps tags some restaurants under generic categories ("Restaurant" instead of "Italian restaurant") even when the cuisine is clear from name and reviews; for full coverage, supplement category matching with name and review-text keyword scanning. Reviews_count freshness — reviews_count updates with a few-hour lag from actual review-posting time; for new-restaurant tracking, snapshot daily during the first 90 days. Multi-cuisine fusion restaurants — fusion restaurants ("Mexican-Korean", "Vietnamese-French") get one primary cuisine tag and may be missed by single-cuisine queries; supplement with explicit fusion queries to capture them.

Thirdwatch's actor handles the scraping plumbing so you can focus on the data. Pair Google Maps with JustDial for India-specific restaurant data and Yelp for US-specific review depth. A fourth subtle issue worth flagging: Google Maps occasionally returns the same restaurant under two place_id values when a business has both a "general" listing and a "primary cuisine" listing (e.g., a sushi restaurant tagged both as "Restaurant" and "Sushi restaurant" in different parts of Google's index). For dedup, fall back to (name, lat, lng) matching after place_id matching to catch these. A fifth pattern unique to restaurant directories: ratings on highly-trafficked tourist-zone restaurants skew 0.3-0.5 stars lower than residential-area peers because the same operator gets harsher reviews from one-time visitors. For cuisine-and-rating directories targeting locals, supplement the rating filter with a reviews_count > 100 and exclude addresses inside designated tourist polygons. A sixth and final pitfall: Google Maps' first-page response gives 20 results for most queries; for deeper discovery (50-100 results per cuisine + city), set maxResults: 100 explicitly — without it, the actor stops at first-page.

Related use cases

• Scrape Google Maps businesses for lead-gen
• Build local-business database with Google Maps
• Scrape business phone and website from Google Maps
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